Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics

Area of Science:

• Orthopaedic surgery
• Artificial intelligence
• Machine learning
• Health data science

Background:

• AI and ML models are gaining traction in Trauma & Orthopaedic surgery for diagnosis and prognosis.
• Understanding these complex models requires specialized knowledge in computing and health data science.
• Inconsistent reporting standards hinder the evaluation of study design and quality in AI/ML research.

Purpose of the Study:

• To provide a clear overview of AI and ML techniques for a lay audience.
• To define key terminology related to AI and ML in a medical context.
• To establish best practice reporting guidelines for AI/ML studies in Trauma & Orthopaedic surgery.

Main Methods:

• Literature review of AI and ML applications in Trauma & Orthopaedic surgery.
• Explanation of fundamental AI and ML concepts in accessible language.
• Development of a framework for reporting AI/ML study methodologies and results.

Main Results:

• The study outlines common AI and ML techniques used in the field.
• Key terminology is defined to facilitate understanding among non-specialists.
• A set of reporting guidelines is proposed to enhance study consistency and quality.

Conclusions:

• Standardized reporting is crucial for the reliable interpretation and application of AI/ML models in Trauma & Orthopaedic surgery.
• Educating clinicians on AI/ML fundamentals and reporting standards will improve research quality.
• Adoption of these guidelines will foster greater trust and utility of AI/ML in clinical practice.